scholarly journals Predictability of flood events in view of current meteorology and hydrology in the conditions of the Czech Republic

2008 ◽  
Vol 2 (No. 4) ◽  
pp. 156-168 ◽  
Author(s):  
L. Březková ◽  
M. Šálek ◽  
E. Soukalová ◽  
M. Starý
Keyword(s):  
Czech Republic ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warning System ◽  
Economic Losses ◽  
Hydrological Models ◽  
Flood Events ◽  
Common Error ◽  
The Czech Republic ◽  
Hydrological Forecast

In central Europe, floods are natural disasters causing the greatest economic losses. One way to reduce partly the flood-related damage, especially the loss of lives, is a functional objective forecasting and warning system that incorporates both meteorological and hydrological models. Numerical weather prediction models operate with horizontal spatial resolution of several dozens of kilometres up to several kilometres, nevertheless, the common error in the localisation of the heavy rainfall characteristic maxima is mostly several times as large as the grid size. The distributive hydrological models for the middle sized basins (hundreds to thousands of km<sup>2</sup>) operate with the resolution of hundreds of meters. Therefore, the (in) accuracy of the meteorological forecast can heavily influence the following hydrological forecast. In general, we can say that the shorter is the duration of the given phenomenon and the smaller area it hits, the more difficult is its prediction. The time and spatial distribution of the predicted precipitation is still one of the most difficult tasks of meteorology. Hydrological forecasts are created under the conditions of great uncertainty. This paper deals with the possibilities of the current hydrology and meteorology with regard to the predictability of the flood events. The Czech Hydrometeorological Institute is responsible by law for the forecasting flood service in the Czech Republic. For the precipitation and temperature forecasts, the outputs of the numerical model of atmosphere ALADIN are used. Moreover, the meteorological community has available operational outputs of many weather prediction models, being run in several meteorological centres around the world. For the hydrological forecast, the HYDROG and AQUALOG models are utilised. The paper shows examples of the hydrological flood forecasts from the years 2002&ndash;2006 in the Dyje catchment, attention being paid to floods caused by heavy rainfalls in the summer season. The results show that it is necessary to take into account the predictability of the particular phenomenon, which can be used in the decision making process during an emergency.

scholarly journals C-Band Dual-Doppler Retrievals in Complex Terrain: Improving the Knowledge of Severe Storm Dynamics in Catalonia

2020 ◽  
Vol 12 (18) ◽  
pp. 2930 ◽  
Author(s):  
Anna del Moral ◽  
Tammy M. Weckwerth ◽  
Tomeu Rigo ◽  
Michael M. Bell ◽  
María Carmen Llasat
Keyword(s):  
Complex Terrain ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warning System ◽  
Wind Fields ◽  
Severe Thunderstorm ◽  
Radar Network ◽  
Storm Dynamics ◽  
Storm Cells ◽  
First Time

Convective activity in Catalonia (northeastern Spain) mainly occurs during summer and autumn, with severe weather occurring 33 days per year on average. In some cases, the storms have unexpected propagation characteristics, likely due to a combination of the complex topography and the thunderstorms’ propagation mechanisms. Partly due to the local nature of the events, numerical weather prediction models are not able to accurately nowcast the complex mesoscale mechanisms (i.e., local influence of topography). This directly impacts the retrieved position and motion of the storms, and consequently, the likely associated storm severity. Although a successful warning system based on lightning and radar observations has been developed, there remains a lack of knowledge of storm dynamics that could lead to forecast improvements. The present study explores the capabilities of the radar network at the Meteorological Service of Catalonia to retrieve dual-Doppler wind fields to study the dynamics of Catalan thunderstorms. A severe thunderstorm that splits and a tornado-producing supercell that is channeled through a valley are used to demonstrate the capabilities of an advanced open source technique that retrieves dynamical variables from C-band operational radars in complex terrain. For the first time in the Iberian Peninsula, complete 3D storm-relative winds are obtained, providing information about the internal dynamics of the storms. This aids in the analyses of the interaction between different storm cells within a system and/or the interaction of the cells with the local topography.

scholarly journals First detection and characterisation of porcine hemagglutinating encephalomyelitis virus in the Czech Republic

2019 ◽  
Vol 64 (No. 02) ◽  
pp. 60-66
Author(s):  
R Moutelikova ◽  
J Prodelalova
Keyword(s):  
Czech Republic ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Phylogenetic Analyses ◽  
Economic Losses ◽  
The Czech Republic ◽  
Nucleocapsid Gene ◽  
Nasal Swabs ◽  
Encephalomyelitis Virus ◽  
Porcine Hemagglutinating Encephalomyelitis Virus

Porcine hemagglutinating encephalomyelitis virus (PHEV) is a highly neurovirulent coronavirus that invades the central nervous system in piglets. The incidence of PHEV among pigs in many countries is rising, and the economic losses to the pig industry may be significant. Serological studies suggest that PHEV is spread worldwide. However, no surveillance has been carried out in the Czech Republic. In this study, eight pig farms were screened for the presence of members of the Coronaviridae family with the use of reverse transcription PCR. A collection of 123 faecal samples and 151 nasal swabs from domestic pigs were analysed. In PHEV-positive samples, almost the complete coding sequence of the nucleocapsid gene was amplified and the acquired sequences were compared to those of geographically dispersed PHEV strains; phylogenetic analyses were also performed. PHEV was present in 7.9% of nasal swabs taken from different age categories of pigs. No other swine coronaviruses were detected. The amino acid sequence of the Czech PHEV strains showed 95.8–98.1% similarity to other PHEV reference strains in GenBank. PHEV strains collected from animals on the same farm were identical; however, strains from different farms have only exhibited only 96.7–98.7% amino acid sequence identity. Our study demonstrates the presence of PHEV in pigs in the Czech Republic. The Czech PHEV strains were evolutionarily closest to the Belgium strain VW572.

scholarly journals Genomic analysis of Escherichia coli strains isolated from diseased chicken in the Czech Republic

2019 ◽  
Author(s):  
Aneta Papoušková ◽  
Martina Masaříková ◽  
Adam Valček ◽  
David Šenk ◽  
Darina Čejková ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Czech Republic ◽  
Economic Losses ◽  
Zoonotic Potential ◽  
Beta Lactamase ◽  
Phylogenetic Groups ◽  
The Czech Republic ◽  
E Coli ◽  
Molecular Features ◽  
Local Spread

Abstract Background: Avian pathogenic Escherichia coli (APEC) can cause various extraintestinal infections in poultry, resulting in massive economic losses in poultry industry. In addition, some avian E. coli strains may have zoonotic potential, making poultry a possible source of infection for humans. Due to its extreme genetic diversity, this pathotype remains poorly defined. This study aimed to investigate the diversity of colibacillosis-associated E. coli isolates from Central European countries with a focus on the Czech Republic. Results: Of 95 clinical isolates subjected to preliminary characterization, 32 were selected for whole-genome sequencing. A multi resistant phenotype was detected in a majority of the sequenced strains with the predominant resistance to β -lactams and quinolones being associated with TEM-type beta-lactamase genes and chromosomal gyrA mutations respectively. The phylogenetic analysis confirmed a great diversity of isolates, that were derived from nearly all phylogenetic groups, with predominace of B2, B1 and C phylogroups. Clusters of closely related isolates within ST23 (phylogroup C) and ST429 (phylogroup B2) indicated a possible local spread of these clones. Besides, the ST429 cluster carried blaCMY-2, -59 genes for AmpC beta-lactamase and isolates of both clusters were generally well-equipped with virulence-associated genes, with considerable differences in distribution of certain virulence-associated genes between phylogenetically distant lineages. Other important and potentially zoonotic APEC STs were detected, incl. ST117, ST354 and ST95, showing several molecular features typical for human ExPEC.Conclusions: The results support the concept of local spread of virulent APEC clones, as well as of zoonotic potential of specific poultry-associated lineages, and highlight the need to investigate the possible source of these pathogenic strains.

scholarly journals Towards Landslides Early Warning System With Fog - Edge Computing And Artificial Intelligence**

2021 ◽  
Vol 15 (02) ◽  
pp. 11-17
Author(s):  
Olivier Debauche ◽  
Meryem Elmoulat ◽  
Saïd Mahmoudi ◽  
Sidi Ahmed Mahmoudi ◽  
Adriano Guttadauria ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Early Warning ◽  
Large Scale ◽  
Prediction Models ◽  
Warning System ◽  
Early Warning Systems ◽  
Economic Losses ◽  
Warning Systems ◽  
Large Scale Data ◽  
Set Up

Landslides are phenomena that cause significant human and economic losses. Researchers have investigated the prediction of high landslides susceptibility with various methodologies based upon statistical and mathematical models, in addition to artificial intelligence tools. These methodologies allow to determine the areas that could present a serious risk of landslides. Monitoring these risky areas is particularly important for developing an Early Warning Systems (EWS). As matter of fact, the variety of landslides’ types make their monitoring a sophisticated task to accomplish. Indeed, each landslide area has its own specificities and potential triggering factors; therefore, there is no single device that can monitor all types of landslides. Consequently, Wireless Sensor Networks (WSN) combined with Internet of Things (IoT) allow to set up large-scale data acquisition systems. In addition, recent advances in Artificial Intelligence (AI) and Federated Learning (FL) allow to develop performant algorithms to analyze this data and predict early landslides events at edge level (on gateways). These algorithms are trained in this case at fog level on specific hardware. The novelty of the work proposed in this paper is the integration of Federated Learning based on Fog-Edge approaches to continuously improve prediction models.

scholarly journals MATISSE: an ArcGIS tool for monitoring and nowcasting meteorological hazards

2015 ◽  
Vol 12 (1) ◽  
pp. 163-169 ◽  
Author(s):  
V. Rillo ◽  
A. L. Zollo ◽  
P. Mercogliano
Keyword(s):  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Meteorological Conditions ◽  
Economic Losses ◽  
Statistical Characterization ◽  
Adverse Weather ◽  
Aviation Hazards ◽  
Meteorological Hazards

Abstract. Adverse meteorological conditions are one of the major causes of accidents in aviation, resulting in substantial human and economic losses. For this reason it is crucial to monitor and early forecast high impact weather events. In this context, CIRA (Italian Aerospace Research Center) has implemented MATISSE (Meteorological AviaTIon Supporting SystEm), an ArcGIS Desktop Plug-in able to detect and forecast meteorological aviation hazards over European airports, using different sources of meteorological data (synoptic information, satellite data, numerical weather prediction models data). MATISSE presents a graphical interface allowing the user to select and visualize such meteorological conditions over an area or an airport of interest. The system also implements different tools for nowcasting of meteorological hazards and for the statistical characterization of typical adverse weather conditions for the airport selected.

FUZZY LOGIC BASED FLASH FLOOD FORECAST

2020 ◽  
Author(s):  
Petr Janál ◽  
◽  
Tomáš Kozel ◽  
Keyword(s):  
Fuzzy Logic ◽  
Czech Republic ◽  
Flash Flood ◽  
Fuzzy Model ◽  
Flood Forecasting ◽  
Hydrological Models ◽  
The Czech Republic ◽  
Flood Model ◽  
Flash Flood Forecasting

The flash flood forecasting remains one of the most difficult tasks in the operative hydrology worldwide. The torrential rainfalls bring high uncertainty included in both forecasted and measured part of the input rainfall data. The hydrological models must be capable to deal with such amount of uncertainty. The artificial intelligence methods work on the principles of adaptability and could represent a proper solution. The application of different methods, approaches, hydrological models and usage of various input data is necessary. The tool for real-time evaluation of the flash flood occurrence was assembled on the bases of the fuzzy logic. The model covers whole area of the Czech Republic and the nearest surroundings. The domain is divided into 3245 small catchments of the average size of 30 km2. Real flood episodes were used for the calibration and future flood events can be used for recalibration (principle of adaptability). The model consists of two fuzzy inference systems (FIS). The catchment predisposition for the flash flood occurrence is evaluated by the first FIS. The geomorphological characteristics and long-term meteorological statistics serve as the inputs. The second FIS evaluates real-time data. The inputs are: The predisposition for flash flood occurrence (gained from the first FIS), the rainfall intensity, the rainfall duration and the antecedent precipitation index. The meteorological radar measurement and the precipitation nowcasting serve as the precipitation data source. Various precipitation nowcasting methods are considered. The risk of the flash flood occurrence is evaluated for each small catchment every 5 or 10 minutes (the time step depends on the precipitation nowcasting method). The Fuzzy Flash Flood model is implemented in the Czech Hydrometeorological Institute (CHMI) – Brno Regional Office. The results are available for all forecasters at CHMI via web application for testing. The huge uncertainty inherent in the flash flood forecasting causes that fuzzy model outputs based on different nowcasting methods could vary significantly. The storms development is very dynamic and hydrological forecast could change a lot of every 5 minutes. That is why the fuzzy model estimates are intended to be used by experts only. The Fuzzy Flash Flood model is an alternative tool for the flash flood forecasting. It can provide the first hints of danger of flash flood occurrence within the whole territory of the Czech Republic. Its main advantage is very fast calculation and possibility of variant approach using various precipitation nowcasting inputs. However, the system produces large number of false alarms, therefore the long-term testing in operation is necessary and the warning releasing rules must be set.

scholarly journals A crude capsular polysaccharide extract as a potential novel subunit vaccine with cross-protection against the most prevalent serovars of Glaesserella (Haemophilus) parasuis in the Czech Republic

2019 ◽  
Vol 64 (No. 9) ◽  
pp. 392-399
Author(s):  
K Matiaskova ◽  
K Nedbalcova ◽  
R Tesarik ◽  
H Kudlackova ◽  
J Gebauer ◽  
...  
Keyword(s):  
Czech Republic ◽  
Western Blotting ◽  
Subunit Vaccine ◽  
Capsular Polysaccharide ◽  
Systemic Disease ◽  
Elisa Test ◽  
Economic Losses ◽  
High Morbidity ◽  
Haemophilus Parasuis ◽  
The Czech Republic

Glaesserella (Haemophilus) parasuis is a part of the normal flora of the respiratory tract of pigs. However, under certain conditions it can also induce severe systemic disease with high morbidity and mortality leading to gross economic losses in the pig industry. The most prevalent serovars in pig herds in the Czech Republic are the virulent serovars 1, 4, 5 and 13. The currently available commercial vaccines are inactivated vaccines with certain limitations, such as no or poor cross-serovar protection. Therefore, the aim of the present study was to construct a subunit vaccine with a crude capsular polysaccharide extract (cCPS) isolated from G. parasuis CAPM 6475 (serovar 5) and evaluate its immunogenicity in a mouse model. Mice were immunised subcutaneously with two doses of the constructed vaccine in a 14-day interval and challenged intraperitoneally with various G. parasuis strains (serovars 1, 4, 5, 13) at 21 days after the second immunisation. The results of the ELISA test showed that the boost dose of the vaccine induced the production of IgG antibodies in high levels. On the basis of the death cases, the pathological findings and the bacterial isolation, the mice immunised with the cCPS were partially protected against the challenge with the homologous serovar 5 as well as with heterologous serovars 1, 4 and 13 of G. parasuis. The cross-reaction of the mixed serum from the immunised mice with the tested serovars was seen in the western-blotting also. Moreover, the most abundant protein found in the cCPS by mass spectrometry was catalase, a protein of molecular weight 55 kDa that may correspond to the strongest reaction seen in the western-blotting. Our findings indicated that the crude capsular polysaccharide extract may provide an effective immunogenicity in preventing a G. parasuis infection caused by the most prevalent serovars in the Czech Republic. However, the evaluation of the efficacy needs to be performed in pigs before any conclusions can be drawn.

scholarly journals Examples of Recent Floods in Europe

2007 ◽  
Vol 2 (3) ◽  
pp. 190-199 ◽  
Author(s):  
Donald Knight ◽  
◽  
Paul Samuels ◽  
Keyword(s):  
Climate Change ◽  
Czech Republic ◽  
Economic Impact ◽  
Economic Impacts ◽  
River Basins ◽  
Climatic Conditions ◽  
Flood Events ◽  
The Czech Republic ◽  
Loss Of Life ◽  
The Uk

Some significant flood events that have occurred in various European countries in the last decade are described. They are used to illustrate the widespread nature of flooding, its economic impact and the resultant loss of life. The underlying hydro-meteorological causes of each flood are outlined, followed by a brief chronology of the flood event and the subsequent consequences. The flood events have been drawn from countries with differing climatic conditions, and from river basins that differ in both size and topography. The selection includes floods from the following countries: the Czech Republic, France, Germany, Hungary, Poland, Switzerland and the UK. The events include examples of both flash floods and slower basin-wide floods. The important lessons that may be drawn from these events are highlighted, as are the economic impacts such floods might have in the future due to climate change.

scholarly journals An Overview of the Performance and Operational Applications of the MRMS and FLASH Systems in Recent Significant Urban Flash Flood Events

2021 ◽  
pp. 1-29
Author(s):  
ALAN GERARD ◽  
STEVEN M. MARTINAITIS ◽  
JONATHAN J. GOURLEY ◽  
KENNETH W. HOWARD ◽  
JIAN ZHANG
Keyword(s):  
Urban Areas ◽  
Situational Awareness ◽  
Prediction Models ◽  
Flash Flood ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Small Time ◽  
Severe Weather ◽  
Flood Events ◽  
The Public

AbstractThe Multi-Radar Multi-Sensor (MRMS) system is an operational, state-of-the-science hydrometeorological data analysis and nowcasting framework that combines data from multiple radar networks, satellites, surface observational systems, and numerical weather prediction models to produce a suite of real-time, decision-support products every two minutes over the contiguous United States and southern Canada. The Flooded Locations and Simulated Hydrograph (FLASH) component of the MRMS system was designed for the monitoring and prediction of flash floods across small time and spatial scales required for urban areas given their rapid hydrologic response to precipitation. Developed at the National Severe Storms Laboratory in collaboration with the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) and other research entities, the objective for MRMS and FLASH is to be the world’s most advanced system for severe weather and storm-scale hydrometeorology, leveraging the latest science and observation systems to produce the most accurate and reliable hydrometeorological and severe weather analyses. NWS forecasters, the public and the private sector utilize a variety of products from the MRMS and FLASH systems for hydrometeorological situational awareness and to provide warnings to the public and other users about potential impacts from flash flooding. This article will examine the performance of hydrometeorological products from MRMS and FLASH, and provide perspectives on how NWS forecasters use these products in the prediction of flash flood events with an emphasis on the urban environment.

Creation of detailed soil properties maps of the Czech Republic based on national legacy data and digital soil mapping

2020 ◽  
Author(s):  
Robert Minařík ◽  
Daniel Žížala ◽  
Anna Juřicová
Keyword(s):  
Czech Republic ◽  
Soil Properties ◽  
Large Scale ◽  
Agricultural Land ◽  
Prediction Models ◽  
External Validation ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
The Czech Republic ◽  
Legacy Data

&lt;p&gt;Legacy soil data arising from traditional soil surveys are an important resource for digital soil mapping. In the Czech Republic, a large-scale (1:10 000) mapping of agricultural land was completed in 1970 after a decade of field investigation mapping. It represents a worldwide unique database of soil samples by its national extent and detail. This study aimed to create a detailed map of soil properties (organic carbon, ph, texture, soil unit) by using state-of-the-art digital soil mapping (DSM) methods. For this purpose we chose four geomorphologically different areas (2440 km&lt;sup&gt;2&lt;/sup&gt; in total). A selected ensemble machine learning techniques based on bagging, boosting and stacking with random hyperparameters tuning were used to model each soil property. In addition to soil sample data, a DEM and its derivatives were used as common covariate layers. The models were evaluated using both internal repeated cross-validation and external validation. The best model was used for prediction of soil properties. The accuracy of prediction models is comparable with other studies. The resulting maps were also compared with the available original soil maps of the Czech Republic. The new maps reveal more spatial detail and natural variability of soil properties resulting from the use of DEM. This combination of high detailed legacy data with DSM results in the production of more spatially detailed and accurate maps, which may be particularly beneficial in supporting the decision-making of stakeholders.&lt;/p&gt;&lt;p&gt;The research has been supported by the project no. QK1820389 &quot; Production of actual detailed maps of soil properties in the Czech Republic based on database of Large-scale Mapping of Agricultural Soils in Czechoslovakia and application of digital soil mapping&quot; funding by Ministry of Agriculture of the Czech Republic.&lt;/p&gt;

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